Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations109127
Missing cells195239
Missing cells (%)8.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.1 MiB
Average record size in memory241.0 B

Variable types

Text1
Categorical8
Numeric12

Alerts

high_conf_clean has constant value "1.0"Constant
feature_006 is highly overall correlated with feature_014High correlation
feature_008 is highly overall correlated with feature_009 and 1 other fieldsHigh correlation
feature_009 is highly overall correlated with feature_008 and 1 other fieldsHigh correlation
feature_010 is highly overall correlated with feature_008 and 1 other fieldsHigh correlation
feature_014 is highly overall correlated with feature_006High correlation
feature_007 is highly imbalanced (61.7%)Imbalance
feature_011 is highly imbalanced (91.7%)Imbalance
feature_012 is highly imbalanced (50.8%)Imbalance
feature_001 has 12817 (11.7%) missing valuesMissing
feature_002 has 12638 (11.6%) missing valuesMissing
feature_003 has 12638 (11.6%) missing valuesMissing
feature_005 has 2311 (2.1%) missing valuesMissing
feature_007 has 2343 (2.1%) missing valuesMissing
feature_008 has 2452 (2.2%) missing valuesMissing
feature_009 has 2452 (2.2%) missing valuesMissing
feature_010 has 2452 (2.2%) missing valuesMissing
feature_011 has 2336 (2.1%) missing valuesMissing
feature_012 has 4516 (4.1%) missing valuesMissing
feature_013 has 2336 (2.1%) missing valuesMissing
feature_014 has 2355 (2.2%) missing valuesMissing
feature_017 has 11378 (10.4%) missing valuesMissing
feature_018 has 11181 (10.2%) missing valuesMissing
high_conf_clean has 45186 (41.4%) missing valuesMissing
is_cheating has 63941 (58.6%) missing valuesMissing
feature_010 is highly skewed (γ1 = 182.1561448)Skewed
user_hash has unique valuesUnique
feature_006 has 18351 (16.8%) zerosZeros
feature_008 has 31159 (28.6%) zerosZeros
feature_009 has 71139 (65.2%) zerosZeros
feature_010 has 51479 (47.2%) zerosZeros
feature_016 has 11378 (10.4%) zerosZeros
feature_018 has 2012 (1.8%) zerosZeros

Reproduction

Analysis started2026-01-17 18:17:45.091510
Analysis finished2026-01-17 18:18:27.882821
Duration42.79 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

user_hash
Text

Unique 

Distinct109127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
2026-01-17T13:18:28.122885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters1746032
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique109127 ?
Unique (%)100.0%

Sample

1st rowd558f000c89bed2a
2nd row1e9668e66b1d6253
3rd rowc0e9e1e943261b5d
4th rowdf3d93556a7b915d
5th row302265f9f7d11aa1
ValueCountFrequency (%)
2ee0108fc5b82c631
 
< 0.1%
f0028eb1ed6399891
 
< 0.1%
d558f000c89bed2a1
 
< 0.1%
1e9668e66b1d62531
 
< 0.1%
c0e9e1e943261b5d1
 
< 0.1%
df3d93556a7b915d1
 
< 0.1%
302265f9f7d11aa11
 
< 0.1%
41e3aad8953a1b391
 
< 0.1%
f643939c4656c10f1
 
< 0.1%
789d73d2194a4e661
 
< 0.1%
Other values (109117)109117
> 99.9%
2026-01-17T13:18:28.581657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5109826
 
6.3%
7109793
 
6.3%
1109735
 
6.3%
a109361
 
6.3%
2109284
 
6.3%
d109097
 
6.2%
c109095
 
6.2%
f109028
 
6.2%
0109015
 
6.2%
6108995
 
6.2%
Other values (6)652803
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1746032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5109826
 
6.3%
7109793
 
6.3%
1109735
 
6.3%
a109361
 
6.3%
2109284
 
6.3%
d109097
 
6.2%
c109095
 
6.2%
f109028
 
6.2%
0109015
 
6.2%
6108995
 
6.2%
Other values (6)652803
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1746032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5109826
 
6.3%
7109793
 
6.3%
1109735
 
6.3%
a109361
 
6.3%
2109284
 
6.3%
d109097
 
6.2%
c109095
 
6.2%
f109028
 
6.2%
0109015
 
6.2%
6108995
 
6.2%
Other values (6)652803
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1746032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5109826
 
6.3%
7109793
 
6.3%
1109735
 
6.3%
a109361
 
6.3%
2109284
 
6.3%
d109097
 
6.2%
c109095
 
6.2%
f109028
 
6.2%
0109015
 
6.2%
6108995
 
6.2%
Other values (6)652803
37.4%

feature_001
Categorical

Missing 

Distinct5
Distinct (%)< 0.1%
Missing12817
Missing (%)11.7%
Memory size7.0 MiB
1.0
33921 
4.0
29544 
5.0
21575 
2.0
6547 
3.0
4723 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters288930
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.033921
31.1%
4.029544
27.1%
5.021575
19.8%
2.06547
 
6.0%
3.04723
 
4.3%
(Missing)12817
 
11.7%

Length

2026-01-17T13:18:28.771231image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-17T13:18:28.984749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.033921
35.2%
4.029544
30.7%
5.021575
22.4%
2.06547
 
6.8%
3.04723
 
4.9%

Most occurring characters

ValueCountFrequency (%)
.96310
33.3%
096310
33.3%
133921
 
11.7%
429544
 
10.2%
521575
 
7.5%
26547
 
2.3%
34723
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)288930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.96310
33.3%
096310
33.3%
133921
 
11.7%
429544
 
10.2%
521575
 
7.5%
26547
 
2.3%
34723
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)288930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.96310
33.3%
096310
33.3%
133921
 
11.7%
429544
 
10.2%
521575
 
7.5%
26547
 
2.3%
34723
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)288930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.96310
33.3%
096310
33.3%
133921
 
11.7%
429544
 
10.2%
521575
 
7.5%
26547
 
2.3%
34723
 
1.6%

feature_002
Real number (ℝ)

Missing 

Distinct10
Distinct (%)< 0.1%
Missing12638
Missing (%)11.6%
Infinite0
Infinite (%)0.0%
Mean4.2640923
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2026-01-17T13:18:29.232918image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q37
95-th percentile9
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.6792322
Coefficient of variation (CV)0.62832417
Kurtosis-1.3163001
Mean4.2640923
Median Absolute Deviation (MAD)1
Skewness0.55755389
Sum411438
Variance7.1782854
MonotonicityNot monotonic
2026-01-17T13:18:29.410457image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
241032
37.6%
811707
 
10.7%
710233
 
9.4%
39642
 
8.8%
97052
 
6.5%
54735
 
4.3%
64453
 
4.1%
13806
 
3.5%
43466
 
3.2%
10363
 
0.3%
(Missing)12638
 
11.6%
ValueCountFrequency (%)
13806
 
3.5%
241032
37.6%
39642
 
8.8%
43466
 
3.2%
54735
 
4.3%
64453
 
4.1%
710233
 
9.4%
811707
 
10.7%
97052
 
6.5%
10363
 
0.3%
ValueCountFrequency (%)
10363
 
0.3%
97052
 
6.5%
811707
 
10.7%
710233
 
9.4%
64453
 
4.1%
54735
 
4.3%
43466
 
3.2%
39642
 
8.8%
241032
37.6%
13806
 
3.5%

feature_003
Real number (ℝ)

Missing 

Distinct10
Distinct (%)< 0.1%
Missing12638
Missing (%)11.6%
Infinite0
Infinite (%)0.0%
Mean6.2237561
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2026-01-17T13:18:29.563487image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median7
Q38
95-th percentile9
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9666429
Coefficient of variation (CV)0.31598971
Kurtosis-0.39345729
Mean6.2237561
Median Absolute Deviation (MAD)1
Skewness-0.57513346
Sum600524
Variance3.8676841
MonotonicityNot monotonic
2026-01-17T13:18:29.736543image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
821541
19.7%
617060
15.6%
717012
15.6%
510653
9.8%
410423
9.6%
99663
8.9%
36009
 
5.5%
22385
 
2.2%
11711
 
1.6%
1032
 
< 0.1%
(Missing)12638
11.6%
ValueCountFrequency (%)
11711
 
1.6%
22385
 
2.2%
36009
 
5.5%
410423
9.6%
510653
9.8%
617060
15.6%
717012
15.6%
821541
19.7%
99663
8.9%
1032
 
< 0.1%
ValueCountFrequency (%)
1032
 
< 0.1%
99663
8.9%
821541
19.7%
717012
15.6%
617060
15.6%
510653
9.8%
410423
9.6%
36009
 
5.5%
22385
 
2.2%
11711
 
1.6%

feature_004
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing1011
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean5.1103167
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2026-01-17T13:18:29.890575image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median6
Q39
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)8

Descriptive statistics

Standard deviation3.6891183
Coefficient of variation (CV)0.72189622
Kurtosis-1.8044433
Mean5.1103167
Median Absolute Deviation (MAD)4
Skewness-0.038256031
Sum552507
Variance13.609594
MonotonicityNot monotonic
2026-01-17T13:18:30.044592image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
140888
37.5%
922816
20.9%
812906
 
11.8%
109530
 
8.7%
78551
 
7.8%
25844
 
5.4%
63171
 
2.9%
32092
 
1.9%
51608
 
1.5%
4710
 
0.7%
(Missing)1011
 
0.9%
ValueCountFrequency (%)
140888
37.5%
25844
 
5.4%
32092
 
1.9%
4710
 
0.7%
51608
 
1.5%
63171
 
2.9%
78551
 
7.8%
812906
 
11.8%
922816
20.9%
109530
 
8.7%
ValueCountFrequency (%)
109530
 
8.7%
922816
20.9%
812906
 
11.8%
78551
 
7.8%
63171
 
2.9%
51608
 
1.5%
4710
 
0.7%
32092
 
1.9%
25844
 
5.4%
140888
37.5%

feature_005
Real number (ℝ)

Missing 

Distinct10
Distinct (%)< 0.1%
Missing2311
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean4.3750562
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2026-01-17T13:18:30.199353image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q37
95-th percentile9
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.7139017
Coefficient of variation (CV)0.62031242
Kurtosis-1.4140472
Mean4.3750562
Median Absolute Deviation (MAD)2
Skewness0.2990346
Sum467326
Variance7.3652624
MonotonicityNot monotonic
2026-01-17T13:18:30.377809image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
227540
25.2%
715827
14.5%
114963
13.7%
310926
 
10.0%
810295
 
9.4%
68992
 
8.2%
97389
 
6.8%
45312
 
4.9%
55213
 
4.8%
10359
 
0.3%
(Missing)2311
 
2.1%
ValueCountFrequency (%)
114963
13.7%
227540
25.2%
310926
 
10.0%
45312
 
4.9%
55213
 
4.8%
68992
 
8.2%
715827
14.5%
810295
 
9.4%
97389
 
6.8%
10359
 
0.3%
ValueCountFrequency (%)
10359
 
0.3%
97389
 
6.8%
810295
 
9.4%
715827
14.5%
68992
 
8.2%
55213
 
4.8%
45312
 
4.9%
310926
 
10.0%
227540
25.2%
114963
13.7%

feature_006
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing896
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean4.4397723
Minimum0
Maximum10
Zeros18351
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2026-01-17T13:18:30.548962image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q37
95-th percentile8
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.0179774
Coefficient of variation (CV)0.6797595
Kurtosis-1.4804395
Mean4.4397723
Median Absolute Deviation (MAD)2
Skewness-0.32735666
Sum480521
Variance9.1081877
MonotonicityNot monotonic
2026-01-17T13:18:30.734056image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
722151
20.3%
619333
17.7%
018351
16.8%
113577
12.4%
813340
12.2%
57373
 
6.8%
26923
 
6.3%
32680
 
2.5%
92282
 
2.1%
42055
 
1.9%
(Missing)896
 
0.8%
ValueCountFrequency (%)
018351
16.8%
113577
12.4%
26923
 
6.3%
32680
 
2.5%
42055
 
1.9%
57373
 
6.8%
619333
17.7%
722151
20.3%
813340
12.2%
92282
 
2.1%
ValueCountFrequency (%)
10166
 
0.2%
92282
 
2.1%
813340
12.2%
722151
20.3%
619333
17.7%
57373
 
6.8%
42055
 
1.9%
32680
 
2.5%
26923
 
6.3%
113577
12.4%

feature_007
Categorical

Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing2343
Missing (%)2.1%
Memory size7.1 MiB
1.0
98822 
0.0
 
7962

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters320352
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.098822
90.6%
0.07962
 
7.3%
(Missing)2343
 
2.1%

Length

2026-01-17T13:18:30.925020image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-17T13:18:31.068646image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.098822
92.5%
0.07962
 
7.5%

Most occurring characters

ValueCountFrequency (%)
0114746
35.8%
.106784
33.3%
198822
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)320352
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0114746
35.8%
.106784
33.3%
198822
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)320352
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0114746
35.8%
.106784
33.3%
198822
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)320352
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0114746
35.8%
.106784
33.3%
198822
30.8%

feature_008
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing2452
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean3.6247012
Minimum0
Maximum10
Zeros31159
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2026-01-17T13:18:31.237351image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.0071017
Coefficient of variation (CV)1.1054985
Kurtosis-1.5133556
Mean3.6247012
Median Absolute Deviation (MAD)1
Skewness0.57578216
Sum386665
Variance16.056864
MonotonicityNot monotonic
2026-01-17T13:18:31.438931image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
031159
28.6%
130688
28.1%
918166
16.6%
109733
 
8.9%
86738
 
6.2%
23667
 
3.4%
72047
 
1.9%
31588
 
1.5%
61424
 
1.3%
41047
 
1.0%
(Missing)2452
 
2.2%
ValueCountFrequency (%)
031159
28.6%
130688
28.1%
23667
 
3.4%
31588
 
1.5%
41047
 
1.0%
5418
 
0.4%
61424
 
1.3%
72047
 
1.9%
86738
 
6.2%
918166
16.6%
ValueCountFrequency (%)
109733
 
8.9%
918166
16.6%
86738
 
6.2%
72047
 
1.9%
61424
 
1.3%
5418
 
0.4%
41047
 
1.0%
31588
 
1.5%
23667
 
3.4%
130688
28.1%

feature_009
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing2452
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean2.4374315
Minimum0
Maximum10
Zeros71139
Zeros (%)65.2%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2026-01-17T13:18:31.616456image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)8

Descriptive statistics

Standard deviation3.9597776
Coefficient of variation (CV)1.6245698
Kurtosis-0.72600408
Mean2.4374315
Median Absolute Deviation (MAD)0
Skewness1.106461
Sum260013
Variance15.679839
MonotonicityNot monotonic
2026-01-17T13:18:31.803973image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
071139
65.2%
920649
 
18.9%
16591
 
6.0%
105414
 
5.0%
21434
 
1.3%
81049
 
1.0%
7171
 
0.2%
379
 
0.1%
665
 
0.1%
463
 
0.1%
(Missing)2452
 
2.2%
ValueCountFrequency (%)
071139
65.2%
16591
 
6.0%
21434
 
1.3%
379
 
0.1%
463
 
0.1%
521
 
< 0.1%
665
 
0.1%
7171
 
0.2%
81049
 
1.0%
920649
 
18.9%
ValueCountFrequency (%)
105414
 
5.0%
920649
18.9%
81049
 
1.0%
7171
 
0.2%
665
 
0.1%
521
 
< 0.1%
463
 
0.1%
379
 
0.1%
21434
 
1.3%
16591
 
6.0%

feature_010
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct5667
Distinct (%)5.3%
Missing2452
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean662.79363
Minimum0
Maximum2899149
Zeros51479
Zeros (%)47.2%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2026-01-17T13:18:32.009557image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q3512
95-th percentile2254
Maximum2899149
Range2899149
Interquartile range (IQR)512

Descriptive statistics

Standard deviation13114.468
Coefficient of variation (CV)19.786654
Kurtosis37487.184
Mean662.79363
Median Absolute Deviation (MAD)3
Skewness182.15614
Sum70703510
Variance1.7198927 × 108
MonotonicityNot monotonic
2026-01-17T13:18:32.254317image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
051479
47.2%
11001
 
0.9%
2604
 
0.6%
3449
 
0.4%
4320
 
0.3%
5295
 
0.3%
7244
 
0.2%
6237
 
0.2%
8211
 
0.2%
9196
 
0.2%
Other values (5657)51639
47.3%
(Missing)2452
 
2.2%
ValueCountFrequency (%)
051479
47.2%
11001
 
0.9%
2604
 
0.6%
3449
 
0.4%
4320
 
0.3%
5295
 
0.3%
6237
 
0.2%
7244
 
0.2%
8211
 
0.2%
9196
 
0.2%
ValueCountFrequency (%)
28991491
< 0.1%
26119181
< 0.1%
8944701
< 0.1%
7829801
< 0.1%
5910001
< 0.1%
4029331
< 0.1%
3961661
< 0.1%
3235801
< 0.1%
2861221
< 0.1%
2424881
< 0.1%

feature_011
Categorical

Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing2336
Missing (%)2.1%
Memory size7.1 MiB
0.0
105689 
1.0
 
1102

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters320373
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0105689
96.8%
1.01102
 
1.0%
(Missing)2336
 
2.1%

Length

2026-01-17T13:18:32.469432image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-17T13:18:32.737294image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0105689
99.0%
1.01102
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0212480
66.3%
.106791
33.3%
11102
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)320373
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0212480
66.3%
.106791
33.3%
11102
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)320373
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0212480
66.3%
.106791
33.3%
11102
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)320373
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0212480
66.3%
.106791
33.3%
11102
 
0.3%

feature_012
Categorical

Imbalance  Missing 

Distinct4
Distinct (%)< 0.1%
Missing4516
Missing (%)4.1%
Memory size7.1 MiB
0.0
84516 
1.0
10728 
2.0
 
5494
3.0
 
3873

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters313833
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.084516
77.4%
1.010728
 
9.8%
2.05494
 
5.0%
3.03873
 
3.5%
(Missing)4516
 
4.1%

Length

2026-01-17T13:18:32.889787image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-17T13:18:33.051461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0.084516
80.8%
1.010728
 
10.3%
2.05494
 
5.3%
3.03873
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0189127
60.3%
.104611
33.3%
110728
 
3.4%
25494
 
1.8%
33873
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)313833
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0189127
60.3%
.104611
33.3%
110728
 
3.4%
25494
 
1.8%
33873
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)313833
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0189127
60.3%
.104611
33.3%
110728
 
3.4%
25494
 
1.8%
33873
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)313833
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0189127
60.3%
.104611
33.3%
110728
 
3.4%
25494
 
1.8%
33873
 
1.2%

feature_013
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing2336
Missing (%)2.1%
Memory size7.1 MiB
1.0
57892 
0.0
48899 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters320373
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.057892
53.1%
0.048899
44.8%
(Missing)2336
 
2.1%

Length

2026-01-17T13:18:33.245040image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-17T13:18:33.416580image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.057892
54.2%
0.048899
45.8%

Most occurring characters

ValueCountFrequency (%)
0155690
48.6%
.106791
33.3%
157892
 
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)320373
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0155690
48.6%
.106791
33.3%
157892
 
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)320373
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0155690
48.6%
.106791
33.3%
157892
 
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)320373
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0155690
48.6%
.106791
33.3%
157892
 
18.1%

feature_014
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing2355
Missing (%)2.2%
Memory size7.1 MiB
1.0
89071 
0.0
17701 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters320316
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.089071
81.6%
0.017701
 
16.2%
(Missing)2355
 
2.2%

Length

2026-01-17T13:18:33.598552image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-17T13:18:33.777044image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.089071
83.4%
0.017701
 
16.6%

Most occurring characters

ValueCountFrequency (%)
0124473
38.9%
.106772
33.3%
189071
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)320316
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0124473
38.9%
.106772
33.3%
189071
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)320316
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0124473
38.9%
.106772
33.3%
189071
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)320316
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0124473
38.9%
.106772
33.3%
189071
27.8%

feature_015
Real number (ℝ)

Distinct68340
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.121775
Minimum-0.87395833
Maximum894.20021
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size1.7 MiB
2026-01-17T13:18:33.964715image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-0.87395833
5-th percentile0.025555556
Q10.039699074
median0.71013889
Q313.882876
95-th percentile133.4555
Maximum894.20021
Range895.07417
Interquartile range (IQR)13.843177

Descriptive statistics

Standard deviation72.961014
Coefficient of variation (CV)2.9042938
Kurtosis41.768502
Mean25.121775
Median Absolute Deviation (MAD)0.68385417
Skewness5.7337383
Sum2741463.9
Variance5323.3096
MonotonicityNot monotonic
2026-01-17T13:18:34.190530image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0325231481538
 
< 0.1%
0.0326851851937
 
< 0.1%
0.0323379629634
 
< 0.1%
0.0325694444434
 
< 0.1%
0.0321759259334
 
< 0.1%
0.0318171296333
 
< 0.1%
0.0304745370433
 
< 0.1%
0.0335995370433
 
< 0.1%
0.0323148148133
 
< 0.1%
0.0351388888932
 
< 0.1%
Other values (68330)108786
99.7%
ValueCountFrequency (%)
-0.87395833331
< 0.1%
0.014143518521
< 0.1%
0.014560185191
< 0.1%
0.01464120371
< 0.1%
0.01473379631
< 0.1%
0.014826388891
< 0.1%
0.014930555561
< 0.1%
0.015034722221
< 0.1%
0.015138888891
< 0.1%
0.015173611111
< 0.1%
ValueCountFrequency (%)
894.20020831
< 0.1%
893.95574071
< 0.1%
888.67031251
< 0.1%
882.68550931
< 0.1%
867.4051
< 0.1%
866.74278941
< 0.1%
866.23715281
< 0.1%
865.80128471
< 0.1%
865.57344911
< 0.1%
865.22113431
< 0.1%

feature_016
Real number (ℝ)

Zeros 

Distinct139
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7829868
Minimum0
Maximum326
Zeros11378
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2026-01-17T13:18:34.401585image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile10
Maximum326
Range326
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.2950305
Coefficient of variation (CV)2.2619692
Kurtosis279.70011
Mean2.7829868
Median Absolute Deviation (MAD)0
Skewness11.932537
Sum303699
Variance39.62741
MonotonicityNot monotonic
2026-01-17T13:18:34.618217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
155792
51.1%
216023
 
14.7%
011378
 
10.4%
37516
 
6.9%
44276
 
3.9%
52908
 
2.7%
62052
 
1.9%
71542
 
1.4%
81074
 
1.0%
9879
 
0.8%
Other values (129)5687
 
5.2%
ValueCountFrequency (%)
011378
 
10.4%
155792
51.1%
216023
 
14.7%
37516
 
6.9%
44276
 
3.9%
52908
 
2.7%
62052
 
1.9%
71542
 
1.4%
81074
 
1.0%
9879
 
0.8%
ValueCountFrequency (%)
3261
< 0.1%
2681
< 0.1%
2561
< 0.1%
2311
< 0.1%
2031
< 0.1%
1991
< 0.1%
1951
< 0.1%
1851
< 0.1%
1811
< 0.1%
1761
< 0.1%

feature_017
Real number (ℝ)

Missing 

Distinct30759
Distinct (%)31.5%
Missing11378
Missing (%)10.4%
Infinite0
Infinite (%)0.0%
Mean13.80772
Minimum0
Maximum24
Zeros18
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2026-01-17T13:18:34.848255image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.9333333
Q19.6166667
median14.666667
Q318.683439
95-th percentile22.546094
Maximum24
Range24
Interquartile range (IQR)9.0667722

Descriptive statistics

Standard deviation6.1495907
Coefficient of variation (CV)0.44537335
Kurtosis-0.67589661
Mean13.80772
Median Absolute Deviation (MAD)4.45
Skewness-0.45046201
Sum1349690.9
Variance37.817466
MonotonicityNot monotonic
2026-01-17T13:18:35.076337image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.3166666787
 
0.1%
16.6333333384
 
0.1%
18.4833333382
 
0.1%
14.6333333381
 
0.1%
17.1581
 
0.1%
16.2666666781
 
0.1%
18.0580
 
0.1%
16.7166666780
 
0.1%
17.6833333379
 
0.1%
14.6666666779
 
0.1%
Other values (30749)96935
88.8%
(Missing)11378
 
10.4%
ValueCountFrequency (%)
018
< 0.1%
0.00013126307921
 
< 0.1%
0.0010160238771
 
< 0.1%
0.0018923087031
 
< 0.1%
0.0041454996751
 
< 0.1%
0.0043546197021
 
< 0.1%
0.0053281503531
 
< 0.1%
0.0055862092621
 
< 0.1%
0.0057718690281
 
< 0.1%
0.0083333333332
 
< 0.1%
ValueCountFrequency (%)
246
< 0.1%
23.998002371
 
< 0.1%
23.994958561
 
< 0.1%
23.994444131
 
< 0.1%
23.993015711
 
< 0.1%
23.991666673
< 0.1%
23.991666672
 
< 0.1%
23.991318661
 
< 0.1%
23.990430871
 
< 0.1%
23.986745921
 
< 0.1%

feature_018
Real number (ℝ)

Missing  Zeros 

Distinct11669
Distinct (%)11.9%
Missing11181
Missing (%)10.2%
Infinite0
Infinite (%)0.0%
Mean0.57458319
Minimum0
Maximum1
Zeros2012
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2026-01-17T13:18:35.329170image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.15000001
Q10.42600001
median0.60000002
Q30.75
95-th percentile0.89999998
Maximum1
Range1
Interquartile range (IQR)0.32399999

Descriptive statistics

Standard deviation0.22776338
Coefficient of variation (CV)0.39639757
Kurtosis-0.29917929
Mean0.57458319
Median Absolute Deviation (MAD)0.15000004
Skewness-0.49464808
Sum56278.125
Variance0.051876156
MonotonicityNot monotonic
2026-01-17T13:18:35.584105image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.54234
 
3.9%
0.753878
 
3.6%
0.80000001193062
 
2.8%
0.60000002382666
 
2.4%
02012
 
1.8%
0.55000001191907
 
1.7%
0.69999998811823
 
1.7%
0.85000002381721
 
1.6%
0.30000001191708
 
1.6%
0.4000000061659
 
1.5%
Other values (11659)73276
67.1%
(Missing)11181
 
10.2%
ValueCountFrequency (%)
02012
1.8%
0.0045454502111
 
< 0.1%
0.0049999998885
 
< 0.1%
0.0060240998863
 
< 0.1%
0.0090909097341
 
< 0.1%
0.0091000003741
 
< 0.1%
0.00999999977622
 
< 0.1%
0.0110999997725
 
< 0.1%
0.01111110028
 
< 0.1%
0.011363649741
 
< 0.1%
ValueCountFrequency (%)
11478
1.4%
0.99629632631
 
< 0.1%
0.99500000481
 
< 0.1%
0.99444401266
 
< 0.1%
0.990000009522
 
< 0.1%
0.98890000581
 
< 0.1%
0.98888900881
 
< 0.1%
0.98888897924
 
< 0.1%
0.985000014310
 
< 0.1%
0.98333349822
 
< 0.1%

high_conf_clean
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing45186
Missing (%)41.4%
Memory size6.9 MiB
1.0
63941 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters191823
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.063941
58.6%
(Missing)45186
41.4%

Length

2026-01-17T13:18:35.811385image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-17T13:18:35.971045image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.063941
100.0%

Most occurring characters

ValueCountFrequency (%)
163941
33.3%
.63941
33.3%
063941
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)191823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
163941
33.3%
.63941
33.3%
063941
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)191823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
163941
33.3%
.63941
33.3%
063941
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)191823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
163941
33.3%
.63941
33.3%
063941
33.3%

is_cheating
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing63941
Missing (%)58.6%
Memory size6.8 MiB
0.0
31414 
1.0
13772 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters135558
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.031414
28.8%
1.013772
 
12.6%
(Missing)63941
58.6%

Length

2026-01-17T13:18:36.129698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-17T13:18:36.280991image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0.031414
69.5%
1.013772
30.5%

Most occurring characters

ValueCountFrequency (%)
076600
56.5%
.45186
33.3%
113772
 
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)135558
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
076600
56.5%
.45186
33.3%
113772
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)135558
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
076600
56.5%
.45186
33.3%
113772
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)135558
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
076600
56.5%
.45186
33.3%
113772
 
10.2%

Interactions

2026-01-17T13:18:23.219323image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:55.063734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:57.460684image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:59.810617image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:02.752041image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:05.352331image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:07.907961image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:10.332174image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:13.042850image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:15.536379image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:18.109367image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:20.947308image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:23.392410image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:55.306306image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:57.674937image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:00.000475image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:02.982090image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:05.580851image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:08.092569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:10.525810image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:13.259408image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:15.776681image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:18.319403image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:21.151901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:23.560157image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:55.492240image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:57.851762image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:00.217863image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:03.204642image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:05.800916image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:08.279882image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:10.861876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:13.456511image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:16.041589image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:18.543017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:21.342933image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:23.741320image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:55.686166image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:58.045526image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:00.475547image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:03.415216image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:05.998023image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:08.468943image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:11.049431image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:13.660176image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:16.266103image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:18.771015image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:21.516303image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:23.927894image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:55.868901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:58.233777image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:00.669483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:03.620764image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:06.193027image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:08.663313image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:11.258011image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:13.843162image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:16.479622image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:19.131662image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:21.691386image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:24.115059image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:56.064571image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:58.437609image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:00.949512image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:03.843294image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:06.412243image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:08.881888image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:11.477576image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:14.044578image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:16.732213image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:19.388892image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:21.872994image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:24.359700image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:56.254618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:58.617740image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:01.157532image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:04.037780image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:06.627289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:09.092921image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:11.785575image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:14.248281image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:16.926961image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:19.596838image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:22.042120image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:24.559750image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:56.436143image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:58.811325image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:01.394089image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:04.246781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:06.849123image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:09.308977image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:12.016650image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:14.465249image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:17.127674image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:19.811370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:22.226226image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:24.767534image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:56.667426image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:59.009745image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:01.633090image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:04.486798image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:07.082660image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:09.529521image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:12.229168image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:14.683685image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:17.337727image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:20.048759image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:22.422302image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:25.004547image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:56.882000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:59.186356image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:01.849129image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:04.701518image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:07.302227image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:09.740454image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:12.430304image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:14.901685image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:17.516209image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:20.250887image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:22.621149image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:25.191147image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:57.059201image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:59.385078image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:02.098146image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:04.918038image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:07.519132image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:09.938941image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:12.643761image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:15.123208image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:17.713648image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:20.468065image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:22.816403image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:25.352643image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:57.256954image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:17:59.583516image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:02.422783image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:05.131686image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:07.711378image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:10.126174image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:12.854293image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:15.337792image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:17.904778image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:20.730678image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2026-01-17T13:18:23.000686image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2026-01-17T13:18:36.432054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
feature_001feature_002feature_003feature_004feature_005feature_006feature_007feature_008feature_009feature_010feature_011feature_012feature_013feature_014feature_015feature_016feature_017feature_018is_cheating
feature_0011.0000.2520.2510.1850.0850.1300.0190.0370.0580.0000.0280.0610.1110.2790.0170.0040.0560.1110.145
feature_0020.2521.000-0.3290.2550.1320.0570.017-0.035-0.0480.0020.0510.0670.1200.1430.0260.005-0.052-0.0420.176
feature_0030.251-0.3291.000-0.436-0.121-0.1280.0220.0280.0440.0120.0110.0560.1250.2030.0220.0880.1180.3510.098
feature_0040.1850.255-0.4361.0000.1300.1110.013-0.038-0.0360.0040.0400.0720.1730.0960.0410.099-0.116-0.3310.251
feature_0050.0850.132-0.1210.1301.000-0.0090.030-0.090-0.064-0.0210.0600.0940.0740.096-0.0080.0330.009-0.0430.143
feature_0060.1300.057-0.1280.111-0.0091.0000.0180.0070.1920.0180.0090.0340.0640.6940.0200.106-0.062-0.0400.158
feature_0070.0190.0170.0220.0130.0300.0181.0000.2380.1700.0000.0000.0420.0110.0250.0220.0040.0000.0080.042
feature_0080.037-0.0350.028-0.038-0.0900.0070.2381.0000.6100.6980.0250.0610.0410.0690.010-0.003-0.0040.0070.117
feature_0090.058-0.0480.044-0.036-0.0640.1920.1700.6101.0000.6040.0260.0410.0330.2690.0120.040-0.0080.0220.116
feature_0100.0000.0020.0120.004-0.0210.0180.0000.6980.6041.0000.0000.0040.0070.0070.0310.010-0.0150.0060.010
feature_0110.0280.0510.0110.0400.0600.0090.0000.0250.0260.0001.0000.0370.0370.0050.0090.0250.0210.0220.079
feature_0120.0610.0670.0560.0720.0940.0340.0420.0610.0410.0040.0371.0000.0560.0640.0110.0000.0140.0220.140
feature_0130.1110.1200.1250.1730.0740.0640.0110.0410.0330.0070.0370.0561.0000.0530.0240.0070.0560.0690.210
feature_0140.2790.1430.2030.0960.0960.6940.0250.0690.2690.0070.0050.0640.0531.0000.0580.0330.0330.1500.195
feature_0150.0170.0260.0220.041-0.0080.0200.0220.0100.0120.0310.0090.0110.0240.0581.0000.2240.0320.0490.133
feature_0160.0040.0050.0880.0990.0330.1060.004-0.0030.0400.0100.0250.0000.0070.0330.2241.0000.0440.0970.026
feature_0170.056-0.0520.118-0.1160.009-0.0620.000-0.004-0.008-0.0150.0210.0140.0560.0330.0320.0441.0000.0730.078
feature_0180.111-0.0420.351-0.331-0.043-0.0400.0080.0070.0220.0060.0220.0220.0690.1500.0490.0970.0731.0000.131
is_cheating0.1450.1760.0980.2510.1430.1580.0420.1170.1160.0100.0790.1400.2100.1950.1330.0260.0780.1311.000

Missing values

2026-01-17T13:18:25.618171image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-17T13:18:26.371588image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-17T13:18:27.303637image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

user_hashfeature_001feature_002feature_003feature_004feature_005feature_006feature_007feature_008feature_009feature_010feature_011feature_012feature_013feature_014feature_015feature_016feature_017feature_018high_conf_cleanis_cheating
34230d558f000c89bed2a1.02.07.07.01.05.01.01.01.021915.00.00.01.01.00.031157110.9333330.5000001.0NaN
1403011e9668e66b1d62531.03.05.01.06.05.01.08.09.0304.00.00.01.01.00.0300230NaN0.4500001.0NaN
143406c0e9e1e943261b5d1.02.07.01.03.07.01.09.09.0242.00.00.01.01.00.0379630NaN0.5000001.0NaN
101176df3d93556a7b915d3.02.07.09.02.07.01.01.00.00.00.00.00.01.010.409340216.8416670.4500001.0NaN
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